Lead Machine Learning Engineer

Established in 1997, Softeq was built from the ground up to specialize in new product development and R&D, tackling the most difficult problems in the tech sphere. Now we've expanded to offer early-stage innovation and ideation plus digital transformation business consulting. Our superpower is to deliver all of this under one roof on a global scale. So let's get started and build a better future together!

Location: Poland/EU countries

Type of contract: B2B (fully remote)

Responsibilities

• Evaluate and adapt state-of-the-art machine learning (ML), computer vision (CV), generative AI, and time series forecasting algorithms to meet product and client objectives.
• Research, design, and implement innovative ML algorithms for image, video, multimodal, and temporal data.
• Architect and develop full-stack ML pipelines—from data acquisition and preprocessing to training, evaluation, and deployment in cloud (AWS) or edge environments.
• Prototype and validate proof-of-concept (POC) solutions for vision, generative AI, and time-series forecasting problems.
• Translate customer requirements into actionable tasks, ensuring a clear understanding of objectives, scope, and expected outcomes.
• Analyze structured and unstructured data to uncover trends, patterns, and anomalies. Apply ML and statistical methods for prediction and forecasting.
• Prepare detailed technical documentation, reports, and presentations for internal and external stakeholders.
• Communicate complex technical topics effectively to both technical and non-technical stakeholders, including clients and business partners.
• Lead projects from prototype to production, ensuring scalability, reliability, and performance of solutions.
• Contribute to internal software development processes and team collaboration initiatives.

Requirements

• Strong hands-on experience in delivering ML solutions, including production-grade computer vision and forecasting models.
• Proven expertise in forecasting and time series data handling (e.g., ARIMA, LSTM, temporal convolutional networks).
• Proficiency in image and video processing, including segmentation, pose estimation, object detection, and multimodal data fusion.
• Experience with generative AI models such as diffusion-based text-to-image/video, multimodal LLMs, and prompt engineering.
• Skilled in reading, interpreting, and applying insights from academic research papers.
• Expertise in deep learning frameworks like PyTorch or TensorFlow.
• Strong object-oriented programming skills with clean, production-quality Python code.
• Familiarity with Vision Transformers (ViTs), especially for action recognition, object tracking, and video understanding tasks.
• Cloud deployment experience, particularly with AWS.
• Excellent communication skills in English (C1 or higher), both written and spoken.
• Strong ability to work independently, prioritize tasks, and manage multiple projects simultaneously.

Nice to Have

• Master’s or Ph.D. degree in Machine Learning, Computer Science, Mathematics, or a related field.
• Contributions to open-source ML or CV libraries or participation in Kaggle competitions.